EB3I n1 2025 scRNAseq
-
PRE-PROCESSING (III)
-
Filtering, cell cyle, cell doublets

1 PREAMBLE

1.1 Purpose of this session

This file describes the different steps to perform the third part of the single cell RNAseq data analysis training course for the EB3I n1 2025, covering these steps :

  • Filtering of bad cells and extreme low-expression features

  • Estimation of the cell cycle phase

  • Identification and removal of cell doublets

2 Start Rstudio

3 Warm-up

  • We set common parameters we will use throughout this session :
# setparam


## Set your project name
# WARNING : Do not just copy-paste this ! It's MY project name ! Put YOURS !!
project_name <- "ebaii_sc_teachers"


## Control if the project_name exists on the cluster
cat('PATH CHECK : ', dir.exists(paste0('/shared/projects/', project_name)))
Show output 
PATH CHECK :  TRUE
## Seed for the RNG
my_seed <- 1337L

## Minimum cells with counts to keep a feature
min_cells <- 5

4 Prepare the data structure

We will do the same as for former steps, just changing the session name.

4.1 Main directory

# maindir

## Preparing the path
TD_dir <- paste0("/shared/projects/", project_name, "/SC_TD")

## Creating the root directory
# dir.create(path = TD_dir, recursive = TRUE)

## Print the root directory on-screen
print(TD_dir)
[1] "/shared/projects/ebaii_sc_teachers/SC_TD"

4.2 Current session

# sessiondir

## Creating the session (Preproc.3) directory
session_dir <- paste0(TD_dir, "/03_Preproc.3")
dir.create(path = session_dir, recursive = TRUE)

## Print the session directory on-screen
print(session_dir)
[1] "/shared/projects/ebaii_sc_teachers/SC_TD/03_Preproc.3"

4.3 Input directory

# indir

## Creating the INPUT data directory
input_dir <- paste0(session_dir, "/DATA")
dir.create(path = input_dir, recursive = TRUE)

## Print the input directory on-screen
print(input_dir)
[1] "/shared/projects/ebaii_sc_teachers/SC_TD/03_Preproc.3/DATA"

4.4 Genelists directory

This is a directory where we will store additional information from knowledge bases about genes used to estimate the cell cycle phase of cells.

# resdir

res_dir <- paste0(TD_dir, "/Resources")
glist_dir <- paste0(res_dir, "/Genelists")

## Create the directory
dir.create(path = glist_dir, recursive = TRUE)

## Print the resources directory on-screen
print(glist_dir)
[1] "/shared/projects/ebaii_sc_teachers/SC_TD/Resources/Genelists"

4.5 Output directory

# outdir

## Creating the OUTPUT data directory
output_dir <- paste0(session_dir, "/RESULTS")
dir.create(path = output_dir, recursive = TRUE)

## Print the output directory on-screen
print(output_dir)
[1] "/shared/projects/ebaii_sc_teachers/SC_TD/03_Preproc.3/RESULTS"

5 Reload the Seurat Object

  • We can reload the object we saved at the former step
# dataload

## The latest Seurat object saved as RDS (name)
sobj_file <- "02_TD3A_S5_Metrics_31053.4587.RDS"

## The latest Seurat object saved as RDS (full path)
sobj_path <- paste0(TD_dir, 
                    "/02_Preproc.2/RESULTS/",
                    sobj_file)

force <- FALSE  ## To force a re-download of a Zenodo-hosted backup
local <- FALSE  ## To force a loading from a local backup

## In case of error/lost data : force a reload from a Zenodo backup repository
if(force) {
  zen_id <- "14035293"
  zen_backup_file <- paste0("https://zenodo.org/records/",
                            zen_id,
                            "/files/",
                            sobj_file)
  ## Recreate the expected path if it does not exist
  dir.create(path = dirname(sobj_path), recursive = TRUE)
  ## Download the file
  download.file(url = zen_backup_file,
                destfile = sobj_path)
}

## In case of error/lost data : force a reload from a local backup repository
if(local) {
  sobj_path <- paste0(
    "/shared/projects/2422_ebaii_n1/atelier_scrnaseq/TD/BACKUP/RDS/",
    sobj_file)
}

## Load the object
sobj <- readRDS(file = sobj_path)



Now, we finally have all the metrics and bias sources we could use, so we can actually get to the filtering step.

6 Filtering

6.1 Features

  • As already explained, the sparsity of the count matrix is high, very high.

  • To the point that there probably are some features that have so low expression level that they were only counted in very few cells. As such, they have absolutely no chance to characterize any cell type, nor harbor some variation between different cell types.

  • As such, we should discard these features


What are the actual dimensions of our expression data ?

# seudim1

## Dimensions of our Seurat object
dim(sobj)
Show output 
[1] 31053  4587


We want to remove the features that are expressed in less than 5 cells.

First, we quantify, for each feature, the number of cells with 0 count.

# nfeatdiff

## Compute the amount of 0s per feature
nFeat_zero <- sparseMatrixStats::rowCounts(
  x = SeuratObject::GetAssayData(
    object = sobj, 
    assay = "RNA", 
    layer = "counts"), 
  value = 0)

## Inversion : computing the number of cells with at least one count, per feature
nFeat_nonzero <- ncol(sobj) - nFeat_zero

## Identify those with at least 5 cells with expression
nFeat_keep <- nFeat_nonzero >= min_cells

## Quantify the selected ones
table(nFeat_keep)
Show output 
nFeat_keep
FALSE  TRUE 
18545 12508


And now we can discard features expressed in less than 5 cells :

# prefeatfiltviz

## Create a temporary object
sobj_VIZ_pre <- sobj

## Usual set of "U can't C me" functions
sobj_VIZ_pre <- Seurat::NormalizeData(object = sobj_VIZ_pre, verbose = FALSE)
sobj_VIZ_pre <- Seurat::ScaleData(object = sobj_VIZ_pre, verbose = FALSE)
sobj_VIZ_pre <- Seurat::FindVariableFeatures(object = sobj_VIZ_pre, verbose = FALSE)
sobj_VIZ_pre <- Seurat::RunPCA(object = sobj_VIZ_pre, npcs = 21, verbose = FALSE)
sobj_VIZ_pre <- Seurat::RunUMAP(object = sobj_VIZ_pre, dims = c(1:20), verbose = FALSE)

## Cell space visualization
dp_pre <- Seurat::DimPlot(
  object = sobj_VIZ_pre, 
  reduction = 'umap') + Seurat::DarkTheme()
print(dp_pre)
Show plot 

## Remove temporary object
rm(sobj_VIZ_pre)


# featsel

## Restrict the Seurat object
sobj <- subset(x = sobj, features = rownames(sobj)[nFeat_keep])


What are the Seurat object dimensions, now ?

# seudim2

dim(sobj)
Show output 
[1] 12508  4587


We can visualize the cell space after this features filtering

# postfeatfiltviz

## Create a temporary object
sobj_VIZ_post <- sobj

## Usual set of "U can't C me" functions
sobj_VIZ_post <- Seurat::NormalizeData(object = sobj_VIZ_post, verbose = FALSE)
sobj_VIZ_post <- Seurat::ScaleData(object = sobj_VIZ_post, verbose = FALSE)
sobj_VIZ_post <- Seurat::FindVariableFeatures(object = sobj_VIZ_post, verbose = FALSE)
sobj_VIZ_post <- Seurat::RunPCA(object = sobj_VIZ_post, npcs = 21, verbose = FALSE)
sobj_VIZ_post <- Seurat::RunUMAP(object = sobj_VIZ_post, dims = c(1:20), verbose = FALSE)

## Cell space visualization
dp_post <- Seurat::DimPlot(
  object = sobj_VIZ_post, 
  reduction = 'umap') + Seurat::DarkTheme()
print(dp_post)
Show plot 

## Remove temporary object
rm(sobj_VIZ_post)


Merging plots for ease of visualization :

# c5_pw

## Using the patchwork package to merge plots (and ggplot2 to add titles)
patchwork::wrap_plots(
  list(
    dp_pre & ggplot2::ggtitle(label = "BEFORE"),
    dp_post & ggplot2::ggtitle(label = "AFTER")), 
  nrow = 1)
Show plot


  • Question :

    # q_mincell

Do you see any difference when comparing before vs after features filtering ?


    # a_mincell

## . Not much changed
##
## . This is expected, as we removed features with almost no expression



6.2 Cells

We are now able to apply all the filtering strategies we established for each QC metric.

  • Apply the filter

    # cellfilt

## Seurat object BEFORE cell filtering
dim(sobj)

    Show output

     
    [1] 12508  4587
    ## Apply cell filtering
sobj <- subset(x = sobj, cells = colnames(sobj)[sobj$fail_qc == "pass"])

## Seurat object AFTER cell filtering
dim(sobj)

    Show output

     
    [1] 12508  4278


We can visualize the cell space since this cell filtering

# postcellfiltviz

## Create a temporary object
sobj_VIZ <- sobj

## Usual set of "U can't C me" functions
sobj_VIZ <- Seurat::NormalizeData(object = sobj_VIZ, verbose = FALSE)
sobj_VIZ <- Seurat::ScaleData(object = sobj_VIZ, verbose = FALSE)
sobj_VIZ <- Seurat::FindVariableFeatures(object = sobj_VIZ, verbose = FALSE)
sobj_VIZ <- Seurat::RunPCA(object = sobj_VIZ, npcs = 21, verbose = FALSE)
sobj_VIZ <- Seurat::RunUMAP(object = sobj_VIZ, dims = c(1:20), verbose = FALSE)

## Cell space visualization
dp_VIZ <- Seurat::DimPlot(
  object = sobj_VIZ, 
  reduction = 'umap') + Seurat::DarkTheme()
print(dp_VIZ)
Show plot 

## Discard the temp object
rm(sobj_VIZ)


One can associate the visualization after filtering cells :

# cfilt_umaps

## Using the patchwork package to merge plots (and ggplot2 to add titles)
patchwork::wrap_plots(
  list(
    dp_post & ggplot2::ggtitle(label = "Features (count<5) filtered"),
    dp_VIZ & ggplot2::ggtitle(label = "Cells filtered (metrics)")), 
  nrow = 1)
Show plot


- Question

```{.r .question}
# q_vizfilt

Do you see any difference when comparing before vs after features filtering ?
```

<br>


```{.r .fold-hide .answer}
# q_postcellfilt

## . Not much changed as well (we did not discard many cells)
##
## . The biggest cluster structure seems more defined
```

<br>

7 Save the Seurat object

We will save our Seurat object that now contains filtered cells and features :

# saverds1

## Save our Seurat object (rich naming)
out_name <- paste0(
          output_dir, "/", paste(
            c("03", Seurat::Project(sobj), "S5", 
              "Metrics.Filtered", paste(
                dim(sobj), 
                collapse = '.'
              )
            ), collapse = "_"),
            ".RDS")

## Check
print(out_name)
[1] "/shared/projects/ebaii_sc_teachers/SC_TD/03_Preproc.3/RESULTS/03_TD3A_S5_Metrics.Filtered_12508.4278.RDS"
## Write on disk
saveRDS(object = sobj, 
        file = out_name)


8 Cell cycle scores

  • We are currently analyzing independent profiles from isolated cells, from a sample dissociation

  • As such, cells were most probably not synchronized, thus the effect of their cell cycle state on genes expression may be strong, to the point that it can bias the data (ie, mask some lower amplitude biological variation).

  • In order to assess (and maybe, remove) this bias, we have to quantify it.

  • We will perform this estimation thanks to heuristics based on knowledge : Seurat includes a method that evaluates the cell cycle phase of cells through scores for the S and G2M phases, each based on phase-specific gene signatures.

  • For this step, we will use additional gene lists from knowledge (cell cycle phase), hosted in a Zenodo respository (Id : 14037355)

8.1 Download gene lists

  • We will directly retrieve data from Zenodo to your input_dir :

    # dlzengl

## Zenodo ID
zen_id <- '14101506'

### Named files (will be used later on !)
cc_file <- "mus_musculus_Seurat_cc.genes_20191031.rds"

## Filename(s) to retrieve
toget_files <- c(cc_file)

## Folder to store retrieved files
local_folder <- glist_dir

## Use local backup ?
backup <- FALSE
if(backup) message("Using local backup !")

## Force download ?
force <- FALSE
if(force) message("Forcing (re)download !")

### Define remote folder
remote_folder <- if (backup) {
  "/shared/projects/2422_ebaii_n1/atelier_scrnaseq/TD/RESOURCES/GENELISTS/"
}  else {
  paste0("https://zenodo.org/records/", zen_id, "/files/")
}

### Reconstruct the input paths
remote_path <- paste0(remote_folder, "/", toget_files)

### Reconstruct the output paths
local_path <- paste0(local_folder, "/", toget_files)

## Retrieve files (if they don't exist), in loop
for (tg in seq_along(toget_files)) {
  ## If the file does not locally exist
  if (!file.exists(local_path[tg]) | force) {
    ## Retrieve data
    if(backup) {
      file.copy(from = remote_path[tg],
                to = local_path[tg])
    } else {
      download.file(url = remote_path[tg], 
                    destfile = local_path[tg])
    }
    ## Check if downloaded files exist locally
    if(file.exists(local_path[tg])) message("\tOK")
  } else message(paste0(toget_files[tg], " already downloaded !"))
}


8.2 Load gene lists

# cc_load

## The cell cycle gene lists file
cc_file <- paste0(glist_dir, 
                  "/", 
                  cc_file)

## Load the cell cycle reference genes lists
cc_genes <- readRDS(file = cc_file)

## Have a look on its content
str(cc_genes)
Show output 
List of 2
 $ s.genes  : chr [1:43] "Mcl5" "Pcna" "Tyms" "Fen1" ...
 $ g2m.genes: chr [1:54] "Hmgb2" "Cdk1" "Nusap1" "Ube2c" ...


  • Question

    # q_cc_check

As explained, we will use gene lists extracted from community knowledge. 
Our data contain values for genes also, so we will cross them. 

Is there something we should check ?


    # a_cc_check

## . We may check if the genes in our gene lists are effectively 
##   present in our Seurat object !
##
## . This is expected, as we removed features with almost no expression


Beyond :

  1. Write a code that performs this check

  2. Add a code to adjust the content of the gene lists accordingly (remove genes from the gene lists that are not present in our dataset)

  3. (NOTE : this is actually not needed as already checked and corrected by the cell cyle estimation method we will use)


# b_cc_answer

## Check if our data genes cover the gene lists
lapply(cc_genes, function(x) x %in% rownames(sobj))

## Remove genes not in sobj
cc_genes <- lapply(cc_genes, function(gl) { gl[gl %in% rownames(sobj)] })

## Check the modification
str(cc_genes)

## Check that all genes are available in sobj
all(unique(unlist(cc_genes)) %in% rownames(sobj))



8.3 Estimation

  • Let’s perform this estimation. But how ?

    # h_CellCycleScoring

## Reading the function help page
?Seurat::CellCycleScoring




  • Run the cell-cycle estimation

    # cc_run

## Creating a temporary object
sobj_CCS <- sobj

sobj_CCS <- Seurat::NormalizeData(object = sobj_CCS, verbose = FALSE)

## Perform the estimation
set.seed(my_seed)
sobj_CCS <- Seurat::CellCycleScoring(
  object = sobj_CCS, 
  s.features = cc_genes$s.genes, 
  g2m.features = cc_genes$g2m.genes, 
  # assay = 'RNA, ## Seurat v4
  layer = 'RNA', ## Seurat v5
  nbin = 24, 
  seed = my_seed)

## Transfering score from the temp object to the real one
sobj$CC_Seurat_S.Score <- sobj_CCS$S.Score
sobj$CC_Seurat_G2M.Score <- sobj_CCS$G2M.Score
sobj$CC_Seurat_Phase <- as.factor(sobj_CCS$Phase)

## Add "S minus G2M" score
sobj$CC_Seurat_SmG2M.Score <- sobj$CC_Seurat_S.Score - sobj$CC_Seurat_G2M.Score

## Discard temp object
rm(sobj_CCS)


  • Description of the object to see the data added

    # desc_cc

View()


8.4 Visualization

As usual, we can visualize the results as violins :

#vlncc

Seurat::VlnPlot(object = sobj,
                features = c("CC_Seurat_S.Score",
                             "CC_Seurat_G2M.Score",
                             "CC_Seurat_SmG2M.Score"))
Show plot


But it’s not that easy to interpret…

Let’s plot it in the cell space

8.4.1 Estimated cell phase :

# vizccp

## Creating a temporary object.
## This time we will keep it to speed up further plots (next chunks)
sobj_VIZ_cc <- sobj

## Usual set of "U can't C me" functions
sobj_VIZ_cc <- Seurat::NormalizeData(object = sobj_VIZ_cc, verbose = FALSE)
sobj_VIZ_cc <- Seurat::ScaleData(object = sobj_VIZ_cc, verbose = FALSE)
sobj_VIZ_cc <- Seurat::FindVariableFeatures(object = sobj_VIZ_cc, verbose = FALSE)
sobj_VIZ_cc <- Seurat::RunPCA(object = sobj_VIZ_cc, npcs = 21, verbose = FALSE)
sobj_VIZ_cc <- Seurat::RunUMAP(object = sobj_VIZ_cc, dims = c(1:20), verbose = FALSE)

## Cell cycle phase feature plot
dp_ccp <- Seurat::DimPlot(
  object = sobj_VIZ_cc, 
  reduction = 'umap', 
  group.by = 'CC_Seurat_Phase') + Seurat::DarkTheme()
print(dp_ccp)
Show plot


8.4.2 SmG2M (“S minus G2M”) score :

# vizccs

## SmG2M feature plot
fp_cc <- Seurat::FeaturePlot(
  object = sobj_VIZ_cc, 
  reduction = 'umap',
  features = 'CC_Seurat_SmG2M.Score') + Seurat::DarkTheme()
print(fp_cc)
Show plot 

## We can discard the temp object
rm(sobj_VIZ_cc)


## {.unnumbered}

Questions

# q_cell1

Does the structure of the cells in this representation seem to have 
a link with these cell cycle phases/scores ?


# a_cell1

## It's an absolute yes !


# q_cell2

Do you think this is the result of an artifact, or
biology-related ?


# a_cell2

##
##    ¯\_(ツ)_/¯
##


9 Save the Seurat object

We will save our Seurat object that now contains the cell cycle states/scores :

# saverds2

## Save our Seurat object (rich naming)
out_name <- paste0(
          output_dir, "/", paste(
            c("04", Seurat::Project(sobj), "S5", 
              "CC", paste(
                dim(sobj), 
                collapse = '.'
              )
            ), collapse = "_"),
            ".RDS")

## Check
print(out_name)
[1] "/shared/projects/ebaii_sc_teachers/SC_TD/03_Preproc.3/RESULTS/04_TD3A_S5_CC_12508.4278.RDS"
## Write on disk
saveRDS(object = sobj, 
        file = out_name)



10 Cell doublets

We will use two different methods to detect and remove cell doublets :

  • scds (in its “hybrid” mode) : more efficient at detecting homotypic doublets

  • scDblFinder : more efficient at detecting heterotypic doublets

None of the methods accepts a SeuratObject as input, but a SingleCellExperiment object.

Hopefully :

  • Seurat has a function to perform the conversion
  • The output results can be integrated into our Seurat object with ease

10.1 Two methods

10.1.1 scds

# scds

## Fix seed
set.seed(my_seed)

## Run scds
sobj$doublet_scds.hybrid <- unname(
  scds::cxds_bcds_hybrid(
    Seurat::as.SingleCellExperiment(
      sobj, assay = "RNA"))$hybrid_score > 1)

## Contingencies
table(sobj$doublet_scds.hybrid)
Show output 

FALSE  TRUE 
 4072   206

10.1.2 scDblFinder

# scdbl

## Fix seed
set.seed(my_seed)

## Run scDblFinder (which needs another object type)
sobj$doublet_scDblFinder <- scDblFinder::scDblFinder(
  sce = Seurat::as.SingleCellExperiment(
    x = sobj, 
    assay = "RNA"),
  returnType = "table")$class == "doublet"

## Contingencies
table(sobj$doublet_scDblFinder)
Show output 

FALSE  TRUE 
 4100   178


10.2 Merge results

We merge results of the two methods

# dblmerge

## Logical union of both methods
sobj$doublet_union <- sobj$doublet_scds.hybrid | sobj$doublet_scDblFinder

## Quantify doublets
table(sobj$doublet_union)
Show output 

FALSE  TRUE 
 4035   243


We can assess tool-specific and common doublets

# dbl_types

### Singlets by default
sobj$doublet_viz <- "singlet"

### Union
sobj$doublet_viz[sobj$doublet_union] <- "both"

### scds-specific
sobj$doublet_viz[sobj$doublet_scds.hybrid & !sobj$doublet_scDblFinder] <- "scds"

### scDblFinder-specific
sobj$doublet_viz[sobj$doublet_scDblFinder & !sobj$doublet_scds.hybrid] <- "scDblFinder"

## Convert to factor
sobj$doublet_viz <- as.factor(sobj$doublet_viz)

## Contingencies
table(sobj$doublet_viz)
Show output 

       both scDblFinder        scds     singlet 
        141          37          65        4035


Beyond : Create an upset-plot for the doublet status according to the two methods used

# b_upset

## Build a list of cells tagged by one tool, the other, or both
dbl_list <-list(
  "scds" = colnames(sobj)[sobj$doublet_scds.hybrid & !sobj$doublet_scDblFinder],
  "scDblFinder" = colnames(sobj)[!sobj$doublet_scds.hybrid & sobj$doublet_scDblFinder],
  "both" = colnames(sobj)[sobj$doublet_scds.hybrid & sobj$doublet_scDblFinder])

## Draw the upset plot
UpSetR::upset(data = UpSetR::fromList(dbl_list), 
              nintersects = NA, 
              sets = rev(names(dbl_list)),
              keep.order = TRUE,
              order.by = "freq")
Show plot


Now we can remove barcodes identified as cell doublets, and visualize the cell space before and after.

10.3 Doublets filtering

10.3.1 BEFORE

# dblviz1

### Doublets viz (before removal)

## Create a temporary object
sobj_VIZ_dbl <- sobj

## Usual set of "U can't C me" functions
sobj_VIZ_dbl <- Seurat::NormalizeData(object = sobj_VIZ_dbl, verbose = FALSE)
sobj_VIZ_dbl <- Seurat::ScaleData(object = sobj_VIZ_dbl, verbose = FALSE)
sobj_VIZ_dbl <- Seurat::FindVariableFeatures(object = sobj_VIZ_dbl, verbose = FALSE)
sobj_VIZ_dbl <- Seurat::RunPCA(object = sobj_VIZ_dbl, npcs = 21, verbose = FALSE)
sobj_VIZ_dbl <- Seurat::RunUMAP(object = sobj_VIZ_dbl, dims = c(1:20), verbose = FALSE)

## Doublets visualization
dp_dbl <- Seurat::DimPlot(
  object = sobj_VIZ_dbl, 
  reduction = 'umap',
  group.by = 'doublet_viz') + Seurat::DarkTheme()

## Plot
print(dp_dbl)
Show plot 

## Discard temp object
rm(sobj_VIZ_dbl)

10.3.2 Remove doublets

# dblrm

## Dimensions before removal
dim(sobj)
[1] 12508  4278
## Perform the removal
# sobj <- sobj[,!sobj$doublet_union]
sobj <- subset(
  x = sobj, 
  cells = colnames(sobj)[sobj$doublet_union], 
  invert = TRUE)

## Dimensions after
dim(sobj)
[1] 12508  4035

10.3.3 AFTER

# dblviz2

### Doublets viz (after removal)

## Create a temporary object
sobj_VIZ_dblf <- sobj

## Usual set of "U can't C me" functions
sobj_VIZ_dblf <- Seurat::NormalizeData(object = sobj_VIZ_dblf, verbose = FALSE)
sobj_VIZ_dblf <- Seurat::ScaleData(object = sobj_VIZ_dblf, verbose = FALSE)
sobj_VIZ_dblf <- Seurat::FindVariableFeatures(object = sobj_VIZ_dblf, verbose = FALSE)
sobj_VIZ_dblf <- Seurat::RunPCA(object = sobj_VIZ_dblf, npcs = 21, verbose = FALSE)
sobj_VIZ_dblf <- Seurat::RunUMAP(object = sobj_VIZ_dblf, dims = c(1:20), verbose = FALSE)

## Doublets-filtered visualization
dp_dblf <- Seurat::DimPlot(
  object = sobj_VIZ_dblf, 
  reduction = 'umap',
  group.by = 'doublet_viz') + Seurat::DarkTheme()

## Plot
print(dp_dblf)
Show plot 

## Discard temp object
rm(sobj_VIZ_dblf)


Merge plots for ease of use :

# dbl_umaps

## Using the patchwork package to merge plots (and ggplot2 to add titles)
patchwork::wrap_plots(
  list(
    dp_dbl & ggplot2::ggtitle(label = "Cell doublets (unfiltered)"),
    dp_dblf & ggplot2::ggtitle(label = "Cell doublets (filtered)")), 
  nrow = 1)
Show plot


Question

# q_dblcomp

What do you observe when comparing before and after the doublets filtering ?
# a_dblcomp

## . Actually not that much !
##
## . This is due to the fact that this dataset did not contain
##   a lot of heterotypic doublets
##
## . When a dataset is heavily contaminated with lots of heterotypic
##   doublets, their removal can drastically remodel the topology.


11 Save the Seurat object

We will save our Seurat object that is now filtered for doublets :

# saverds3

## Save our Seurat object (rich naming)
out_name <- paste0(
          output_dir, "/", paste(
            c("05", Seurat::Project(sobj), "S5", 
              "Doublets.Filtered", paste(
                dim(sobj), 
                collapse = '.'
              )
            ), collapse = "_"),
            ".RDS")

## Check
print(out_name)
Show output 
[1] "/shared/projects/ebaii_sc_teachers/SC_TD/03_Preproc.3/RESULTS/05_TD3A_S5_Doublets.Filtered_12508.4035.RDS"
## Write on disk
saveRDS(object = sobj, 
        file = out_name)





12 Rsession

# rsession

utils::sessionInfo()
Show output 
R version 4.4.1 (2024-06-14)
Platform: x86_64-conda-linux-gnu
Running under: Ubuntu 22.04.5 LTS

Matrix products: default
BLAS/LAPACK: /shared/ifbstor1/software/miniconda/envs/r-4.4.1/lib/libopenblasp-r0.3.29.so;  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Europe/Paris
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] SeuratObject_5.1.0 sp_2.2-0          

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.22            splines_4.4.1              
  [3] later_1.4.2                 BiocIO_1.14.0              
  [5] bitops_1.0-9                tibble_3.2.1               
  [7] polyclip_1.10-7             pROC_1.18.5                
  [9] XML_3.99-0.18               fastDummies_1.7.5          
 [11] lifecycle_1.0.4             scDblFinder_1.18.0         
 [13] edgeR_4.2.2                 globals_0.18.0             
 [15] lattice_0.22-6              MASS_7.3-65                
 [17] magrittr_2.0.3              limma_3.60.6               
 [19] plotly_4.10.4               sass_0.4.10                
 [21] rmarkdown_2.29              jquerylib_0.1.4            
 [23] yaml_2.3.10                 metapod_1.12.0             
 [25] httpuv_1.6.15               Seurat_5.3.0               
 [27] sctransform_0.4.2           spam_2.11-1                
 [29] spatstat.sparse_3.1-0       reticulate_1.42.0          
 [31] cowplot_1.1.3               pbapply_1.7-2              
 [33] RColorBrewer_1.1-3          abind_1.4-8                
 [35] zlibbioc_1.50.0             Rtsne_0.17                 
 [37] GenomicRanges_1.56.2        purrr_1.0.4                
 [39] RCurl_1.98-1.17             BiocGenerics_0.50.0        
 [41] GenomeInfoDbData_1.2.12     IRanges_2.38.1             
 [43] S4Vectors_0.42.1            ggrepel_0.9.6              
 [45] irlba_2.3.5.1               listenv_0.9.1              
 [47] spatstat.utils_3.1-4        goftest_1.2-3              
 [49] RSpectra_0.16-2             dqrng_0.4.1                
 [51] spatstat.random_3.4-1       fitdistrplus_1.2-2         
 [53] parallelly_1.45.0           DelayedMatrixStats_1.26.0  
 [55] codetools_0.2-20            DelayedArray_0.30.1        
 [57] scuttle_1.14.0              tidyselect_1.2.1           
 [59] UCSC.utils_1.0.0            farver_2.1.2               
 [61] viridis_0.6.5               ScaledMatrix_1.12.0        
 [63] matrixStats_1.5.0           stats4_4.4.1               
 [65] rmdformats_1.0.4            spatstat.explore_3.4-3     
 [67] GenomicAlignments_1.40.0    jsonlite_2.0.0             
 [69] BiocNeighbors_1.22.0        progressr_0.15.1           
 [71] scater_1.32.1               ggridges_0.5.6             
 [73] survival_3.7-0              tools_4.4.1                
 [75] ica_1.0-3                   Rcpp_1.0.14                
 [77] glue_1.8.0                  gridExtra_2.3              
 [79] SparseArray_1.4.8           xfun_0.52                  
 [81] MatrixGenerics_1.16.0       GenomeInfoDb_1.40.1        
 [83] dplyr_1.1.4                 withr_3.0.2                
 [85] fastmap_1.2.0               bluster_1.14.0             
 [87] digest_0.6.37               rsvd_1.0.5                 
 [89] R6_2.6.1                    mime_0.13                  
 [91] scattermore_1.2             tensor_1.5                 
 [93] dichromat_2.0-0.1           spatstat.data_3.1-6        
 [95] UpSetR_1.4.0                tidyr_1.3.1                
 [97] generics_0.1.4              data.table_1.17.4          
 [99] rtracklayer_1.64.0          httr_1.4.7                 
[101] htmlwidgets_1.6.4           S4Arrays_1.4.1             
[103] uwot_0.2.3                  pkgconfig_2.0.3            
[105] gtable_0.3.6                lmtest_0.9-40              
[107] SingleCellExperiment_1.26.0 XVector_0.44.0             
[109] htmltools_0.5.8.1           dotCall64_1.2              
[111] bookdown_0.39               scales_1.4.0               
[113] Biobase_2.64.0              png_0.1-8                  
[115] spatstat.univar_3.1-3       scran_1.32.0               
[117] knitr_1.50                  rstudioapi_0.17.1          
[119] rjson_0.2.23                reshape2_1.4.4             
[121] curl_6.2.3                  nlme_3.1-165               
[123] cachem_1.1.0                zoo_1.8-14                 
[125] stringr_1.5.1               KernSmooth_2.23-24         
[127] parallel_4.4.1              miniUI_0.1.2               
[129] vipor_0.4.7                 restfulr_0.0.15            
[131] ggrastr_1.0.2               pillar_1.10.2              
[133] grid_4.4.1                  vctrs_0.6.5                
[135] RANN_2.6.2                  promises_1.3.2             
[137] BiocSingular_1.20.0         beachmat_2.20.0            
[139] xtable_1.8-4                cluster_2.1.6              
[141] beeswarm_0.4.0              scds_1.20.0                
[143] evaluate_1.0.3              locfit_1.5-9.9             
[145] cli_3.6.5                   compiler_4.4.1             
[147] Rsamtools_2.20.0            rlang_1.1.6                
[149] crayon_1.5.3                future.apply_1.11.3        
[151] labeling_0.4.3              plyr_1.8.9                 
[153] ggbeeswarm_0.7.2            stringi_1.8.7              
[155] viridisLite_0.4.2           deldir_2.0-4               
[157] BiocParallel_1.38.0         Biostrings_2.72.1          
[159] lazyeval_0.2.2              spatstat.geom_3.4-1        
[161] Matrix_1.7-3                RcppHNSW_0.6.0             
[163] patchwork_1.3.0             sparseMatrixStats_1.16.0   
[165] future_1.49.0               ggplot2_3.5.2              
[167] statmod_1.5.0               shiny_1.10.0               
[169] SummarizedExperiment_1.34.0 ROCR_1.0-11                
[171] igraph_2.1.4                bslib_0.9.0                
[173] xgboost_1.7.11.1
---
title: "<CENTER>EB3I n1 2025 scRNAseq<BR>-<BR><B>PRE-PROCESSING (III)</B><BR>-<BR>Filtering, cell cyle, cell doublets</CENTER>"
date: "2025-16-21.22"
author:
  - name: "EB3I n1 scRNAseq Team"
  - name: "Bastien JOB"
    email: "bastien.job@gustaveroussy.fr"
  - name: "Lilia YOUNSI"
    email: "lilia.younsi@inserm.fr" 
output:
  rmdformats::readthedown:
    fig_width: 8
    fig_height: 6
    highlight: tango  ## Theme for the code chunks
    embed_fonts: TRUE
    number_sections: true  ## Adds number to headers (sections)
    theme: flatly  ## CSS theme for the HTML page
    collapsed: true  ## By default, the TOC is folded
    toc_depth: 3
    smooth_scroll: true ## Smooth scroll of the HTML page
    self_contained: true ## Includes all plots/images within the HTML
    code_download: true ## Adds a button to download the Rmd
    code_folding: show
    thumbnails: false
    lightbox: true
    fig_caption: false
    gallery: true
    use_bookdown: true
always_allow_html: true ## Allow plain HTML code in the Rmd
editor_options: 
  markdown: 
    wrap: 72
---

<!-- knit setup -->

```{r knit_setup, echo = FALSE}
knitr::opts_chunk$set(
  echo = TRUE,        # Print the code
  eval = TRUE,        # Run command lines
  message = FALSE,    # Print messages
  prompt = FALSE,     # Do not display prompt
  comment = NA,       # No comments on this section
  warning = FALSE,    # Display warnings
  tidy = FALSE,
  fig.align="center", 
  # results = 'hide',
  width = 100       # Number of characters per line
)
```

<!-- CSS to color chunks and outputs -->

```{css, echo=FALSE}
.notrun {
  background-color: lightgrey !important;
  border: 3px solid black !important;
}
.notruno {
  background-color: lightgrey !important;
  color : black !important;
}
.question {
  background-color: aquamarine !important;
  color : black !important;
  border: 3px solid limegreen !important;
}
.questiono {
  background-color: aquamarine !important;
  color : black !important;
}
.answer {
  background-color: navajowhite !important;
  border: 3px solid brown !important;
}
.answero {
  background-color: navajowhite !important;
  color : black !important;
}
.beyond {
  background-color: violet !important;
  border: 3px solid purple !important;
}
.beyondo {
  background-color: violet !important;
  color : black !important;
}
```

<!-- Hook to handle code blocks output folding -->

```{r knit_hook, echo = FALSE}
hooks = knitr::knit_hooks$get()
hook_foldable = function(type) {
  force(type)
  function(x, options) {
    res = hooks[[type]](x, options)
    
    if (isFALSE(options[[paste0("fold.", type)]])) return(res)
    
    paste0(
      "<details><summary>Show ", type, "</summary>\n\n",
      res,
      "\n\n</details>"
    )
  }
}
knitr::knit_hooks$set(
  output = hook_foldable("output"),
  plot = hook_foldable("plot")
)
```

------------------------------------------------------------------------

------------------------------------------------------------------------

<center>![](eb3i_banner.png)</center>

------------------------------------------------------------------------

------------------------------------------------------------------------

# PREAMBLE

## Purpose of this session

This file describes the different steps to perform the **third part** of
the single cell RNAseq data analysis training course for the **EB3I n1
2025**, covering these steps :

-   **Filtering** of bad cells and extreme low-expression features

-   Estimation of the **cell cycle** phase

-   Identification and removal of **cell doublets**

------------------------------------------------------------------------

------------------------------------------------------------------------

# Start Rstudio

-   Using the [OpenOnDemand/Rstudio cheat
    sheet](https://moodle.france-bioinformatique.fr/pluginfile.php/1475/mod_folder/content/0/OoD_R_Rstudio.html){target="_blank"},
    connect to the [OpenOnDemand
    portal](https://ondemand.cluster.france-bioinformatique.fr){target="_blank"} and
    **create a Rstudio session** with the right resource requirements.

------------------------------------------------------------------------

------------------------------------------------------------------------

# Warm-up

-   We set **common parameters** we will use throughout this session :

```{r setparam}
# setparam


## Set your project name
# WARNING : Do not just copy-paste this ! It's MY project name ! Put YOURS !!
project_name <- "ebaii_sc_teachers"


## Control if the project_name exists on the cluster
cat('PATH CHECK : ', dir.exists(paste0('/shared/projects/', project_name)))

## Seed for the RNG
my_seed <- 1337L

## Minimum cells with counts to keep a feature
min_cells <- 5

```

------------------------------------------------------------------------

------------------------------------------------------------------------

# Prepare the data structure

We will do the same as for former steps, just changing the session name.

## Main directory

```{r maindir, fold.output = FALSE}
# maindir

## Preparing the path
TD_dir <- paste0("/shared/projects/", project_name, "/SC_TD")

## Creating the root directory
# dir.create(path = TD_dir, recursive = TRUE)

## Print the root directory on-screen
print(TD_dir)

```

## Current session

```{r sessiondir, fold.output = FALSE}
# sessiondir

## Creating the session (Preproc.3) directory
session_dir <- paste0(TD_dir, "/03_Preproc.3")
dir.create(path = session_dir, recursive = TRUE)

## Print the session directory on-screen
print(session_dir)

```

## Input directory

```{r indir, fold.output = FALSE}
# indir

## Creating the INPUT data directory
input_dir <- paste0(session_dir, "/DATA")
dir.create(path = input_dir, recursive = TRUE)

## Print the input directory on-screen
print(input_dir)

```

## Genelists directory

This is a directory where we will store additional information from 
knowledge bases about genes used to estimate the cell cycle phase of cells.

```{r resdir, fold.output = FALSE}
# resdir

res_dir <- paste0(TD_dir, "/Resources")
glist_dir <- paste0(res_dir, "/Genelists")

## Create the directory
dir.create(path = glist_dir, recursive = TRUE)

## Print the resources directory on-screen
print(glist_dir)

```

## Output directory

```{r outdir, fold.output = FALSE}
# outdir

## Creating the OUTPUT data directory
output_dir <- paste0(session_dir, "/RESULTS")
dir.create(path = output_dir, recursive = TRUE)

## Print the output directory on-screen
print(output_dir)

```

------------------------------------------------------------------------

------------------------------------------------------------------------

# Reload the Seurat Object

-   We can reload the object we saved at the former step

```{r dataload}
# dataload

## The latest Seurat object saved as RDS (name)
sobj_file <- "02_TD3A_S5_Metrics_31053.4587.RDS"

## The latest Seurat object saved as RDS (full path)
sobj_path <- paste0(TD_dir, 
                    "/02_Preproc.2/RESULTS/",
                    sobj_file)

force <- FALSE  ## To force a re-download of a Zenodo-hosted backup
local <- FALSE  ## To force a loading from a local backup

## In case of error/lost data : force a reload from a Zenodo backup repository
if(force) {
  zen_id <- "14035293"
  zen_backup_file <- paste0("https://zenodo.org/records/",
                            zen_id,
                            "/files/",
                            sobj_file)
  ## Recreate the expected path if it does not exist
  dir.create(path = dirname(sobj_path), recursive = TRUE)
  ## Download the file
  download.file(url = zen_backup_file,
                destfile = sobj_path)
}

## In case of error/lost data : force a reload from a local backup repository
if(local) {
  sobj_path <- paste0(
    "/shared/projects/2422_ebaii_n1/atelier_scrnaseq/TD/BACKUP/RDS/",
    sobj_file)
}

## Load the object
sobj <- readRDS(file = sobj_path)

```

<br><br>

Now, we finally have all the metrics and bias sources we could
use, so we can actually get to the filtering step.


# Filtering

## Features

-   As already explained, the **sparsity** of the count matrix is high, _very_
high. 

-   To the point that there probably are some features that have **so
low expression** level that they were only counted in **very few cells**. As
such, they have absolutely **no chance** to characterize any cell type, nor
harbor some variation between different cell types.

-   As such, we should **discard** these features

<br>

What are the actual dimensions of our expression data ?

```{r seudim1}
# seudim1

## Dimensions of our Seurat object
dim(sobj)

```

<br>

We want to remove the features that are expressed in less than **`r min_cells`**
cells.

First, we quantify, for each feature, the number of cells with `0` count.

```{r nfeatdiff}
# nfeatdiff

## Compute the amount of 0s per feature
nFeat_zero <- sparseMatrixStats::rowCounts(
  x = SeuratObject::GetAssayData(
    object = sobj, 
    assay = "RNA", 
    layer = "counts"), 
  value = 0)

## Inversion : computing the number of cells with at least one count, per feature
nFeat_nonzero <- ncol(sobj) - nFeat_zero

## Identify those with at least 5 cells with expression
nFeat_keep <- nFeat_nonzero >= min_cells

## Quantify the selected ones
table(nFeat_keep)

```

<br>

And now we can **discard** features expressed in less than `r min_cells` cells :

```{r prefeatfiltviz, class.source="notrun", class.output="notruno"}
# prefeatfiltviz

## Create a temporary object
sobj_VIZ_pre <- sobj

## Usual set of "U can't C me" functions
sobj_VIZ_pre <- Seurat::NormalizeData(object = sobj_VIZ_pre, verbose = FALSE)
sobj_VIZ_pre <- Seurat::ScaleData(object = sobj_VIZ_pre, verbose = FALSE)
sobj_VIZ_pre <- Seurat::FindVariableFeatures(object = sobj_VIZ_pre, verbose = FALSE)
sobj_VIZ_pre <- Seurat::RunPCA(object = sobj_VIZ_pre, npcs = 21, verbose = FALSE)
sobj_VIZ_pre <- Seurat::RunUMAP(object = sobj_VIZ_pre, dims = c(1:20), verbose = FALSE)

## Cell space visualization
dp_pre <- Seurat::DimPlot(
  object = sobj_VIZ_pre, 
  reduction = 'umap') + Seurat::DarkTheme()
print(dp_pre)

## Remove temporary object
rm(sobj_VIZ_pre)

```

<br>

```{r featsel}
# featsel

## Restrict the Seurat object
sobj <- subset(x = sobj, features = rownames(sobj)[nFeat_keep])

```

<br>

What are the Seurat object dimensions, now ?

```{r seudim2, class.source="notrun", class.output="notruno"}
# seudim2

dim(sobj)

```

<br>

We can **visualize the cell space** after this features filtering

```{r postfeatfiltviz}
# postfeatfiltviz

## Create a temporary object
sobj_VIZ_post <- sobj

## Usual set of "U can't C me" functions
sobj_VIZ_post <- Seurat::NormalizeData(object = sobj_VIZ_post, verbose = FALSE)
sobj_VIZ_post <- Seurat::ScaleData(object = sobj_VIZ_post, verbose = FALSE)
sobj_VIZ_post <- Seurat::FindVariableFeatures(object = sobj_VIZ_post, verbose = FALSE)
sobj_VIZ_post <- Seurat::RunPCA(object = sobj_VIZ_post, npcs = 21, verbose = FALSE)
sobj_VIZ_post <- Seurat::RunUMAP(object = sobj_VIZ_post, dims = c(1:20), verbose = FALSE)

## Cell space visualization
dp_post <- Seurat::DimPlot(
  object = sobj_VIZ_post, 
  reduction = 'umap') + Seurat::DarkTheme()
print(dp_post)

## Remove temporary object
rm(sobj_VIZ_post)

```

<br>

Merging plots for ease of visualization :

```{r c5_pw, fig.width = 15, fig.height = 6, class.source=c("fold-hide", "notrun"), class.output="notruno"}
# c5_pw

## Using the patchwork package to merge plots (and ggplot2 to add titles)
patchwork::wrap_plots(
  list(
    dp_pre & ggplot2::ggtitle(label = "BEFORE"),
    dp_post & ggplot2::ggtitle(label = "AFTER")), 
  nrow = 1)

```

<br>

-   **Question** : 
    
    ```{r q_mincell, eval = FALSE, class.source="question", eval = FALSE}
    # q_mincell
    
    Do you see any difference when comparing before vs after features filtering ?
    
    ```
    
    <br>
    
    ```{r a_mincell, class.source = c("fold-hide", "answer"), eval = FALSE}
    # a_mincell
    
    ## . Not much changed
    ##
    ## . This is expected, as we removed features with almost no expression
    ```
    
<br><br>

## Cells

We are now able to **apply all the filtering strategies** we established for
each QC metric.


-   Apply the filter

    ```{r cellfilt}
    # cellfilt
    
    ## Seurat object BEFORE cell filtering
    dim(sobj)
    
    ## Apply cell filtering
    sobj <- subset(x = sobj, cells = colnames(sobj)[sobj$fail_qc == "pass"])
    
    ## Seurat object AFTER cell filtering
    dim(sobj)
    
    ```

<br>

We can **visualize the cell space** since this cell filtering

```{r postcellfiltviz}
# postcellfiltviz

## Create a temporary object
sobj_VIZ <- sobj

## Usual set of "U can't C me" functions
sobj_VIZ <- Seurat::NormalizeData(object = sobj_VIZ, verbose = FALSE)
sobj_VIZ <- Seurat::ScaleData(object = sobj_VIZ, verbose = FALSE)
sobj_VIZ <- Seurat::FindVariableFeatures(object = sobj_VIZ, verbose = FALSE)
sobj_VIZ <- Seurat::RunPCA(object = sobj_VIZ, npcs = 21, verbose = FALSE)
sobj_VIZ <- Seurat::RunUMAP(object = sobj_VIZ, dims = c(1:20), verbose = FALSE)

## Cell space visualization
dp_VIZ <- Seurat::DimPlot(
  object = sobj_VIZ, 
  reduction = 'umap') + Seurat::DarkTheme()
print(dp_VIZ)

## Discard the temp object
rm(sobj_VIZ)

```

<br>

One can associate the visualization after filtering cells :

```{r cfilt_umaps, fig.width = 12, fig.height = 6, class.source="notrun", class.output="notruno"}
# cfilt_umaps

## Using the patchwork package to merge plots (and ggplot2 to add titles)
patchwork::wrap_plots(
  list(
    dp_post & ggplot2::ggtitle(label = "Features (count<5) filtered"),
    dp_VIZ & ggplot2::ggtitle(label = "Cells filtered (metrics)")), 
  nrow = 1)

```

<br>
-   **Question**
    
    ```{r q_vizfilt, eval = FALSE, class.source="question", eval = FALSE}
    # q_vizfilt
    
    Do you see any difference when comparing before vs after features filtering ?
    
    ```
    
    <br>
    
    ```{r q_postcellfilt, class.source = c("fold-hide", "answer"), eval = FALSE}
    # q_postcellfilt
    
    ## . Not much changed as well (we did not discard many cells)
    ##
    ## . The biggest cluster structure seems more defined
    
    ```
    
    <br>

------------------------------------------------------------------------

------------------------------------------------------------------------

# Save the Seurat object

We will **save our Seurat object** that now contains **filtered cells and
features** :

```{r saverds1, fold.output = FALSE}
# saverds1

## Save our Seurat object (rich naming)
out_name <- paste0(
          output_dir, "/", paste(
            c("03", Seurat::Project(sobj), "S5", 
              "Metrics.Filtered", paste(
                dim(sobj), 
                collapse = '.'
              )
            ), collapse = "_"),
            ".RDS")

## Check
print(out_name)

## Write on disk
saveRDS(object = sobj, 
        file = out_name)

```
<br>

------------------------------------------------------------------------

------------------------------------------------------------------------

# Cell cycle scores

-   We are currently analyzing **independent** profiles from  **isolated** cells, from a sample dissociation

-   As such, cells were most probably **not synchronized**, thus the effect of their **cell cycle state** on genes expression may be strong, to the point that it can **bias the data** (ie, mask some lower amplitude biological variation).

-   In order to **assess** (and maybe, _remove_) this bias, we have to **quantify** it.

-   We will perform this estimation thanks to heuristics based on **knowledge** : Seurat includes a method that evaluates the cell cycle phase of cells through **scores for the S and G2M phases**, each based on phase-specific **gene signatures**.

-   For this step, we will use additional gene lists from knowledge
    (cell cycle phase), hosted in a Zenodo respository (Id :
    [14037355](https://zenodo.org/records/14037355 "Zenodo gene lists"){target="_blank"})

## Download gene lists

-   We will directly retrieve data from Zenodo to your `input_dir` :

    ```{r dlzengl}
    # dlzengl
    
    ## Zenodo ID
    zen_id <- '14101506'
    
    ### Named files (will be used later on !)
    cc_file <- "mus_musculus_Seurat_cc.genes_20191031.rds"
    
    ## Filename(s) to retrieve
    toget_files <- c(cc_file)
    
    ## Folder to store retrieved files
    local_folder <- glist_dir
    
    ## Use local backup ?
    backup <- FALSE
    if(backup) message("Using local backup !")
    
    ## Force download ?
    force <- FALSE
    if(force) message("Forcing (re)download !")
    
    ### Define remote folder
    remote_folder <- if (backup) {
      "/shared/projects/2422_ebaii_n1/atelier_scrnaseq/TD/RESOURCES/GENELISTS/"
    }  else {
      paste0("https://zenodo.org/records/", zen_id, "/files/")
    }
    
    ### Reconstruct the input paths
    remote_path <- paste0(remote_folder, "/", toget_files)
    
    ### Reconstruct the output paths
    local_path <- paste0(local_folder, "/", toget_files)
    
    ## Retrieve files (if they don't exist), in loop
    for (tg in seq_along(toget_files)) {
      ## If the file does not locally exist
      if (!file.exists(local_path[tg]) | force) {
        ## Retrieve data
        if(backup) {
          file.copy(from = remote_path[tg],
                    to = local_path[tg])
        } else {
          download.file(url = remote_path[tg], 
                        destfile = local_path[tg])
        }
        ## Check if downloaded files exist locally
        if(file.exists(local_path[tg])) message("\tOK")
      } else message(paste0(toget_files[tg], " already downloaded !"))
    }
    
    ```

<br>

## Load gene lists

```{r cc_load}
# cc_load

## The cell cycle gene lists file
cc_file <- paste0(glist_dir, 
                  "/", 
                  cc_file)

## Load the cell cycle reference genes lists
cc_genes <- readRDS(file = cc_file)

## Have a look on its content
str(cc_genes)

```

<br>

-   **Question**
    
    ```{r q_cc_check, class.source="question", eval = FALSE}
    # q_cc_check
    
    As explained, we will use gene lists extracted from community knowledge. 
    Our data contain values for genes also, so we will cross them. 
    
    Is there something we should check ?
    
    ```
    
    <br>
    
    ```{r a_cc_check, class.source = c("fold-hide", "answer"), eval = FALSE}
    # a_cc_check
    
    ## . We may check if the genes in our gene lists are effectively 
    ##   present in our Seurat object !
    ##
    ## . This is expected, as we removed features with almost no expression
    
    ```
    
    <br>

**Beyond** :

1.  Write a code that performs this check

2.  Add a code to adjust the content of the gene lists accordingly (remove genes from the gene lists that are not present in our dataset)

3.  *(NOTE : this is actually not needed as already checked and
    corrected by the cell cyle estimation method we will use)*

<br>

```{r b_cc_answer, class.source = c("fold-hide", "beyond"), class.output="beyondo", eval = FALSE}
# b_cc_answer

## Check if our data genes cover the gene lists
lapply(cc_genes, function(x) x %in% rownames(sobj))

## Remove genes not in sobj
cc_genes <- lapply(cc_genes, function(gl) { gl[gl %in% rownames(sobj)] })

## Check the modification
str(cc_genes)

## Check that all genes are available in sobj
all(unique(unlist(cc_genes)) %in% rownames(sobj))

```

<br><br>

## Estimation

-   Let's perform this estimation. But how ?

    ```{r h_CellCycleScoring, class.source = "notrun", eval = FALSE}
    # h_CellCycleScoring
    
    ## Reading the function help page
    ?Seurat::CellCycleScoring
    
    ```
    
<br><br><br>

-   Run the cell-cycle estimation

    ```{r cc_run}
    # cc_run
    
    ## Creating a temporary object
    sobj_CCS <- sobj
    
    sobj_CCS <- Seurat::NormalizeData(object = sobj_CCS, verbose = FALSE)
    
    ## Perform the estimation
    set.seed(my_seed)
    sobj_CCS <- Seurat::CellCycleScoring(
      object = sobj_CCS, 
      s.features = cc_genes$s.genes, 
      g2m.features = cc_genes$g2m.genes, 
      # assay = 'RNA, ## Seurat v4
      layer = 'RNA', ## Seurat v5
      nbin = 24, 
      seed = my_seed)
    
    ## Transfering score from the temp object to the real one
    sobj$CC_Seurat_S.Score <- sobj_CCS$S.Score
    sobj$CC_Seurat_G2M.Score <- sobj_CCS$G2M.Score
    sobj$CC_Seurat_Phase <- as.factor(sobj_CCS$Phase)
    
    ## Add "S minus G2M" score
    sobj$CC_Seurat_SmG2M.Score <- sobj$CC_Seurat_S.Score - sobj$CC_Seurat_G2M.Score
    
    ## Discard temp object
    rm(sobj_CCS)
    ```

<br>

-   Description of the object to see the data added

    ```{r desc_cc, class.source="notrun", class.output="notruno", eval = FALSE}
    # desc_cc
    
    View()
    
    ```

<br>

## Visualization 

As usual, we can visualize the results as violins :

```{r vlncc, fig.width = 12, class.source="notrun", class.output="notruno"}
#vlncc

Seurat::VlnPlot(object = sobj,
                features = c("CC_Seurat_S.Score",
                             "CC_Seurat_G2M.Score",
                             "CC_Seurat_SmG2M.Score"))

```

<br>

But it's not that easy to interpret... 

Let's plot it in the cell space

## {.unnumbered .tabset .tabset-fade .tabset-pills}

### Estimated cell phase :

```{r vizccp}
# vizccp

## Creating a temporary object.
## This time we will keep it to speed up further plots (next chunks)
sobj_VIZ_cc <- sobj

## Usual set of "U can't C me" functions
sobj_VIZ_cc <- Seurat::NormalizeData(object = sobj_VIZ_cc, verbose = FALSE)
sobj_VIZ_cc <- Seurat::ScaleData(object = sobj_VIZ_cc, verbose = FALSE)
sobj_VIZ_cc <- Seurat::FindVariableFeatures(object = sobj_VIZ_cc, verbose = FALSE)
sobj_VIZ_cc <- Seurat::RunPCA(object = sobj_VIZ_cc, npcs = 21, verbose = FALSE)
sobj_VIZ_cc <- Seurat::RunUMAP(object = sobj_VIZ_cc, dims = c(1:20), verbose = FALSE)

## Cell cycle phase feature plot
dp_ccp <- Seurat::DimPlot(
  object = sobj_VIZ_cc, 
  reduction = 'umap', 
  group.by = 'CC_Seurat_Phase') + Seurat::DarkTheme()
print(dp_ccp)

```

<br>

### SmG2M ("S minus G2M") score :

```{r vizccs}
# vizccs

## SmG2M feature plot
fp_cc <- Seurat::FeaturePlot(
  object = sobj_VIZ_cc, 
  reduction = 'umap',
  features = 'CC_Seurat_SmG2M.Score') + Seurat::DarkTheme()
print(fp_cc)

## We can discard the temp object
rm(sobj_VIZ_cc)

```

<br>
## {.unnumbered}

**Questions**

```{r q_cell1, class.source="question", eval = FALSE}
# q_cell1

Does the structure of the cells in this representation seem to have 
a link with these cell cycle phases/scores ?

```

<br>

```{r a_cell1, class.source = c("fold-hide", "answer"), eval = FALSE}
# a_cell1

## It's an absolute yes !

```

<br>

```{r q_cell2, class.source="question", eval = FALSE}
# q_cell2

Do you think this is the result of an artifact, or
biology-related ?

```

<br>

```{r a_cell2, class.source = c("fold-hide", "answer"), eval = FALSE}
# a_cell2

##
##    ¯\_(ツ)_/¯
##

```

<br>

------------------------------------------------------------------------

------------------------------------------------------------------------

# Save the Seurat object

We will save our Seurat object that now contains the cell cycle
states/scores :

```{r saverds2, fold.output = FALSE}
# saverds2

## Save our Seurat object (rich naming)
out_name <- paste0(
          output_dir, "/", paste(
            c("04", Seurat::Project(sobj), "S5", 
              "CC", paste(
                dim(sobj), 
                collapse = '.'
              )
            ), collapse = "_"),
            ".RDS")

## Check
print(out_name)

## Write on disk
saveRDS(object = sobj, 
        file = out_name)

```

<br>

------------------------------------------------------------------------

------------------------------------------------------------------------

<br>

# Cell doublets

We will use **two different methods** to detect and remove cell doublets :

-   [`scds`](https://www.bioconductor.org/packages/release/bioc/html/scds.html){target="_blank"} (in its "hybrid" mode) : more efficient at detecting **homotypic** doublets

-   [`scDblFinder`](https://www.bioconductor.org/packages/release/bioc/html/scDblFinder.html){target="_blank"} : more efficient at detecting **heterotypic** doublets

None of the methods accepts a `SeuratObject` as input, but a [`SingleCellExperiment`](https://www.bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html){target="_blank"} object.

Hopefully :

-   Seurat has a function to perform the conversion
-   The output results can be integrated into our Seurat object with ease

## Two methods {.tabset .tabset-fade .tabset-pills}

### `scds`

```{r scds}
# scds

## Fix seed
set.seed(my_seed)

## Run scds
sobj$doublet_scds.hybrid <- unname(
  scds::cxds_bcds_hybrid(
    Seurat::as.SingleCellExperiment(
      sobj, assay = "RNA"))$hybrid_score > 1)

## Contingencies
table(sobj$doublet_scds.hybrid)

```


### `scDblFinder`

```{r scdbl}
# scdbl

## Fix seed
set.seed(my_seed)

## Run scDblFinder (which needs another object type)
sobj$doublet_scDblFinder <- scDblFinder::scDblFinder(
  sce = Seurat::as.SingleCellExperiment(
    x = sobj, 
    assay = "RNA"),
  returnType = "table")$class == "doublet"

## Contingencies
table(sobj$doublet_scDblFinder)

```

#  {.unnumbered}

------------------------------------------------------------------------

------------------------------------------------------------------------

<br>

## Merge results

We merge results of the two methods

```{r dblmerge}
# dblmerge

## Logical union of both methods
sobj$doublet_union <- sobj$doublet_scds.hybrid | sobj$doublet_scDblFinder

## Quantify doublets
table(sobj$doublet_union)

```

<br>

We can assess **tool-specific** and **common** doublets

```{r dbl_types, class.source="notrun", class.output="notruno"}
# dbl_types

### Singlets by default
sobj$doublet_viz <- "singlet"

### Union
sobj$doublet_viz[sobj$doublet_union] <- "both"

### scds-specific
sobj$doublet_viz[sobj$doublet_scds.hybrid & !sobj$doublet_scDblFinder] <- "scds"

### scDblFinder-specific
sobj$doublet_viz[sobj$doublet_scDblFinder & !sobj$doublet_scds.hybrid] <- "scDblFinder"

## Convert to factor
sobj$doublet_viz <- as.factor(sobj$doublet_viz)

## Contingencies
table(sobj$doublet_viz)

```

<br>

**Beyond** : Create an upset-plot for the doublet status according to the two methods used

```{r b_upset, class.source=c("fold-hide", "beyond"), class.output="beyondo"}
# b_upset

## Build a list of cells tagged by one tool, the other, or both
dbl_list <-list(
  "scds" = colnames(sobj)[sobj$doublet_scds.hybrid & !sobj$doublet_scDblFinder],
  "scDblFinder" = colnames(sobj)[!sobj$doublet_scds.hybrid & sobj$doublet_scDblFinder],
  "both" = colnames(sobj)[sobj$doublet_scds.hybrid & sobj$doublet_scDblFinder])

## Draw the upset plot
UpSetR::upset(data = UpSetR::fromList(dbl_list), 
              nintersects = NA, 
              sets = rev(names(dbl_list)),
              keep.order = TRUE,
              order.by = "freq")

```

<br>

Now we can remove barcodes identified as cell doublets, and visualize the cell space before and after.

## Doublets filtering {.tabset .tabset-fade .tabset-pills}

### BEFORE

```{r dblviz1}
# dblviz1

### Doublets viz (before removal)

## Create a temporary object
sobj_VIZ_dbl <- sobj

## Usual set of "U can't C me" functions
sobj_VIZ_dbl <- Seurat::NormalizeData(object = sobj_VIZ_dbl, verbose = FALSE)
sobj_VIZ_dbl <- Seurat::ScaleData(object = sobj_VIZ_dbl, verbose = FALSE)
sobj_VIZ_dbl <- Seurat::FindVariableFeatures(object = sobj_VIZ_dbl, verbose = FALSE)
sobj_VIZ_dbl <- Seurat::RunPCA(object = sobj_VIZ_dbl, npcs = 21, verbose = FALSE)
sobj_VIZ_dbl <- Seurat::RunUMAP(object = sobj_VIZ_dbl, dims = c(1:20), verbose = FALSE)

## Doublets visualization
dp_dbl <- Seurat::DimPlot(
  object = sobj_VIZ_dbl, 
  reduction = 'umap',
  group.by = 'doublet_viz') + Seurat::DarkTheme()

## Plot
print(dp_dbl)

## Discard temp object
rm(sobj_VIZ_dbl)

```

### Remove doublets

```{r dblrm, fold.output = FALSE}
# dblrm

## Dimensions before removal
dim(sobj)

## Perform the removal
# sobj <- sobj[,!sobj$doublet_union]
sobj <- subset(
  x = sobj, 
  cells = colnames(sobj)[sobj$doublet_union], 
  invert = TRUE)

## Dimensions after
dim(sobj)

```

### AFTER

```{r dblviz2, class.source = "notrun", class.output="notruno"}
# dblviz2

### Doublets viz (after removal)

## Create a temporary object
sobj_VIZ_dblf <- sobj

## Usual set of "U can't C me" functions
sobj_VIZ_dblf <- Seurat::NormalizeData(object = sobj_VIZ_dblf, verbose = FALSE)
sobj_VIZ_dblf <- Seurat::ScaleData(object = sobj_VIZ_dblf, verbose = FALSE)
sobj_VIZ_dblf <- Seurat::FindVariableFeatures(object = sobj_VIZ_dblf, verbose = FALSE)
sobj_VIZ_dblf <- Seurat::RunPCA(object = sobj_VIZ_dblf, npcs = 21, verbose = FALSE)
sobj_VIZ_dblf <- Seurat::RunUMAP(object = sobj_VIZ_dblf, dims = c(1:20), verbose = FALSE)

## Doublets-filtered visualization
dp_dblf <- Seurat::DimPlot(
  object = sobj_VIZ_dblf, 
  reduction = 'umap',
  group.by = 'doublet_viz') + Seurat::DarkTheme()

## Plot
print(dp_dblf)

## Discard temp object
rm(sobj_VIZ_dblf)

```

##  {.unnumbered}

------------------------------------------------------------------------

------------------------------------------------------------------------

<br>

Merge plots for ease of use :

```{r dbl_umaps, fig.width = 12, fig.height = 6, class.source = "notrun", class.output="notruno"}
# dbl_umaps

## Using the patchwork package to merge plots (and ggplot2 to add titles)
patchwork::wrap_plots(
  list(
    dp_dbl & ggplot2::ggtitle(label = "Cell doublets (unfiltered)"),
    dp_dblf & ggplot2::ggtitle(label = "Cell doublets (filtered)")), 
  nrow = 1)

```

<br>

**Question**

```{r q_dblcomp, class.source="question", eval=FALSE}
# q_dblcomp

What do you observe when comparing before and after the doublets filtering ?

```

```{r a_dblcomp, class.source = c("fold-hide", "answer"), eval=FALSE}
# a_dblcomp

## . Actually not that much !
##
## . This is due to the fact that this dataset did not contain
##   a lot of heterotypic doublets
##
## . When a dataset is heavily contaminated with lots of heterotypic
##   doublets, their removal can drastically remodel the topology.

```

<br>

------------------------------------------------------------------------

------------------------------------------------------------------------


# Save the Seurat object

We will save our Seurat object that is now filtered for doublets :

```{r saverds3}
# saverds3

## Save our Seurat object (rich naming)
out_name <- paste0(
          output_dir, "/", paste(
            c("05", Seurat::Project(sobj), "S5", 
              "Doublets.Filtered", paste(
                dim(sobj), 
                collapse = '.'
              )
            ), collapse = "_"),
            ".RDS")

## Check
print(out_name)

## Write on disk
saveRDS(object = sobj, 
        file = out_name)

```

<br>

------------------------------------------------------------------------

------------------------------------------------------------------------

<br><br><br>

# Rsession

```{r rsession, class.source="notrun", class.output="notruno"}
# rsession

utils::sessionInfo()

```
